Authors : Gowsalya S; Dr. Subatra Devi

Volume/Issue : Volume 10 - 2025, Issue 7 - July

Google Scholar : https://tinyurl.com/4s8mn6na

Scribd : https://tinyurl.com/4znuatkp

DOI : https://doi.org/10.38124/ijisrt/25jul1768

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : The aloft complexity of deepfake technology has sparked serious concerns across domains including journalism, cybersecurity, political discourse, and digital identity. Fueled by advancements in deep learning, synthetic media can now convincingly mimic human expressions, voice patterns, and behaviours, challenging the boundaries of trust in multimedia content. This paper provides a comprehensive investigation into state-of-the-art detection methods across video, audio, and multimodal formats. By categorizing leading approaches—including convolutional networks, spectrogram-based analysis, and cross-modal consistency frameworks—we expose technical limitations in scalability, generalization, and explainability. Additionally, we highlight gaps in ethical governance and the absence of cross-industry standards to regulate deepfake mitigation. The study advocates for evolving detection strategies rooted in adversarial robustness, multimodal fusion, and privacy-aware learning. Through this interdisciplinary lens, we chart a roadmap for the next generation of deepfake detection systems capable of safeguarding digital authenticity without compromising civil liberties. The insights presented herein aim to empower researchers, policymakers, and platform developers to co-create resilient, future-ready defences against synthetic manipulation.

References :

1. Heidari A, Jafari Navimipour N, Dag H, Unal M. Deepfake detection using deep learning methods: a systematic and comprehensive review. Wiley Interdis Rev. 2024;14(2): e1520.
2. Sun G, Zhang Y, Yu H, Du X, Guizani M. Intersection fog-based distributed routing for V2V communication in urban vehicular ad hoc networks. IEEE Trans Intell Transp Syst. 2020;21(6):2409–26. https://doi.org/10.1109/TITS.2019.2918255.
3. Sun G, Song L, Yu H, Chang V, Du X, Guizani M. V2V routing in a VANET based on the autoregressive integrated moving average model. IEEE Trans Vehicul Technol. 2019;68(1):908–22. https://doi.org/10.1109/TVT.2018.2884525.
4. Sun G, Zhang Y, Liao D, Yu H, Du X, Guizani M. Bus-trajectory-based street-centric routing for message delivery in urban vehicular ad hoc networks. IEEE Trans Veh Technol. 2018;67(8):7550–63. https://doi.org/10.1109/TVT.2018.282865.
5. Khan, A. A., Laghari, A. A., Alroobaea, R., Baqasah, A. M., Alsafyani, M., Bacarra, R., & Alsayaydeh, J. A. J. (2024). Secure Remote Sensing Data With Blockchain Distributed Ledger Technology: A Solution for Smart Cities. IEEE Access.
6. Ullah S, Qiao X, Abbas M. Addressing the impact of land use land cover changes on land surface temperature using machine learning algorithms. Sci Rep. 2024;14:18746. https://doi.org/10.1038/s41598-024-68492-7.
7. Khan AA, Wagan AA, Laghari AA, Gilal AR, Aziz IA, Talpur BA. BIoMT: A state-of-the-art consortium serverless network architecture for healthcare system using blockchain smart contracts. IEEE Access. 2022;10:78887–98.
8. Ullah S, Abbas M, Qiao X. Impact assessment of land-use alteration on land surface temperature in Kabul using machine learning algo- rithm. J Spat Sci. 2024;70:1–23. https://doi.org/10.1080/14498596.2024.2364283.
9. Xia J, Li S, Huang J, Yang Z, Jaimoukha IM, Gündüz D. Metalearning-Based Alternating Minimization Algorithm for Nonconvex Optimiza- tion. IEEE Trans Neu Net Learn Sys. 2023;34(9):5366–80. https://doi.org/10.1109/TNNLS.2022.3165627.
10. Khan AA, Dhabi S, Yang J, Alhakami W, Bourouis S, Yee L. B-LPoET: A middleware lightweight Proof-of-Elapsed Time (PoET) for efficient distributed transaction execution and security on Blockchain using multithreading technology. Comput Electr Eng. 2024;118:109343.
11. Ullah S, Qiao X, Tariq A. Impact assessment of planned and unplanned urbanization on land surface temperature in Afghanistan using machine learning algorithms: a path toward sustainability. Sci Rep. 2025;15:3092. https://doi.org/10.1038/s41598-025-87234-x.
12. Han F, Yang P, Du H, Li X. Accuth+: Accelerometer-Based Anti-Spoofing Voice Authentication on Wrist-Worn Wearables. IEEE Trans Mob Comput. 2024;23(5):5571–88. https://doi.org/10.1109/TMC.2023.3314837.
13. Zuo C, Zhang X, Yan L, Zhang Z. GUGEN: Global User Graph Enhanced Network for Next POI Recommendation. IEEE Trans Mob Comput. 2024;23(12):14975–86. https://doi.org/10.1109/TMC.2024.3455107.
14. Yang K. How to prevent deception: A study of digital deception in “visual poverty” livestream. New Media Soc. 2024. https://doi.org/10. 1177/14614448241285443.
15. Khan AA, Laghari AA, Baqasah AM, Alroobaea R, Almadhor A, Sampedro GA, Kryvinska N. Blockchain-enabled infrastructural security solution for serverless consortium fog and edge computing. PeerJ ComputSci. 2024;10:e1933.
16. Li C, He A, Liu G, Wen Y, Chronopoulos AT, Giannakos A. RFL-APIA: a comprehensive framework for mitigating poisoning attacks and promoting model aggregation in IIoT federated learning. IEEE Trans Industr Inf. 2024;20(11):12935–44. https://doi.org/10.1109/TII.2024. 3431020.
17. Lin, W., Xia, C., Wang, T., Zhao, Y., Xi, L., … Zhang, S. (2024). Input and Output Matter: Malicious Traffic Detection with Explainability. IEEE Networkhttps://doi.org/10.1109/MNET.2024.3481045
18. Khan AA, Chen YL, Hajjej F, Shaikh AA, Yang J, Ku CS, Por LY. Digital forensics for the socio-cyber world (DF-SCW): A novel framework for deepfake multimedia investigation on social media platforms. Egypt Informat J. 2024;27:100502.
19. Heidari A, Navimipour NJ, Dag H, Talebi S, Unal M. A novel blockchain-based deepfake detection method using federated and deep learning models. Cogn Comput. 2024;16:1073–91.
20. Haq IU, Malik KM, Muhammad K. Multimodal neurosymbolic approach for explainable deepfake detection. ACM Trans Multimed Comput Commun Appl. 2024;20(11):1–16.